Development of QSAR model based on Monte Carlo optimization for predicting GABAA receptor binding of newly emerging benzodiazepines.

Benzodiazepines and their derivatives belong to a category of new psychoactive substances that have been introduced into the continually expanding illicit market. However, there is a notable absence of available pharmacological data for these substances. To gain a deeper understanding of their pharmacology, we employed the Monte Carlo optimization conformation-independent method as a tool for developing QSAR models. These models were built using optimal molecular descriptors derived from both SMILES notation and molecular graph representations. The resulting QSAR model demonstrated robustness and a high degree of predictability, proving to be very reliable. Moreover, we were able to identify specific molecular fragments that exerted both positive and negative effects on binding activity. This discovery paves the way for the swift prediction of binding activity for emerging benzodiazepines, offering a faster and more cost-effective alternative to traditional in vitro/in vivo analyses.

A comprehensive assessment of the chemical composition, antioxidant, genoprotective and antigenotoxic activities of Lamiaceae species using different experimental models in vitro.

This research was aimed to assess the potential of Glechoma hederacea, Hyssopus officinalis, Lavandula angustifolia, Leonurus cardiaca, Marrubium vulgare and Sideritis scardica (Lamiaceae) methanolic, ethanolic and aqueous extracts against the damaging effects of oxidative stress using different experimental models. The chemical characterization was done spectrophotometrically by quantifying total phenolics, phenolic acids, flavonoids and flavonols in the extracts, as well as by employing HPLC-DAD technique. Moreover, DPPH assay was used to assess the extracts' radical scavenging potential. Genoprotective properties of the extracts were evaluated using plasmid pUC19 Escherichia coli XL1-Blue, whereas their antigenotoxic potential was determined using Salmonella typhimurium TA1535/pSK1002 and normal human lung fibroblasts. All of the extracts showed antioxidant activity in DPPH assay. Furthermore, the results have shown that aqueous extracts provided the best protection for plasmid DNA, while alcoholic extracts most effectively contributed to the preservation of prokaryotic DNA. Additionally, each of the tested samples significantly protected the eukaryotic cells against genomic damages. Finally, despite not showing exceptional results in DPPH assay, S. scardica extracts are regarded as the most favorable in maintaining the integrity of DNA, which might be due to high quantities of phenolics such as quercetin (up to 17.95 mg g-1), naringin (up to 5.07 mg g-1) and luteolin-7-O-glucoside (up to 3.54 mg g-1). Overall, this comprehensive concept highlights the ability of these Lamiaceae species to safeguard the DNA from reactive oxygen species, to curtail the inflicted damage and also improve the efficiency of the DNA repair mechanisms, while emphasizing the importance of polyphenols as their active principles.

Adherence to Mediterranean Diet and Maternal Lifestyle during Pregnancy: Island-Mainland Differentiation in the CRIBS Birth Cohort.

Maternal nutrition and lifestyle in pregnancy are important modifiable factors for both maternal and offspring's health. Although the Mediterranean diet has beneficial effects on health, recent studies have shown low adherence in Europe. This study aimed to assess the Mediterranean diet adherence in 266 pregnant women from Dalmatia, Croatia and to investigate their lifestyle habits and regional differences. Adherence to the Mediterranean diet was assessed through two Mediterranean diet scores. Differences in maternal characteristics (diet, education, income, parity, smoking, pre-pregnancy body mass index (BMI), physical activity, contraception) with regards to location and dietary habits were analyzed using the non-parametric Mann-Whitney U test. The machine learning approach was used to reveal other potential non-linear relationships. The results showed that adherence to the Mediterranean diet was low to moderate among the pregnant women in this study, with no significant mainland-island differences. The highest adherence was observed among wealthier women with generally healthier lifestyle choices. The most significant mainland-island differences were observed for lifestyle and socioeconomic factors (income, education, physical activity). The machine learning approach confirmed the findings of the conventional statistical method. We can conclude that adverse socioeconomic and lifestyle conditions were more pronounced in the island population, which, together with the observed non-Mediterranean dietary pattern, calls for more effective intervention strategies.

Chemical Profiling and Assessment of Antineurodegenerative and Antioxidant Properties of Veronica teucrium L. and Veronica jacquinii Baumg.

Neuroprotective potential of V. teucrium and V. jacquinii methanol extracts was analyzed. Chemical analysis of investigated extracts showed the presence of phenolic acid derivatives, flavonoids and one secoiridoid. The detected flavonoids derived from flavones (luteolin and isoscutellarein in V. jacquinii; apigenin, isoscutellarein and luteolin in V. teucrium) and flavonol (quercetin in V. jacquinii). Acteoside was the dominant compound in V. jacquinii, while plantamajoside and isoscutellarein 7-O-(6‴-O-acetyl)-ß-allosyl (1‴→2‴)-ß-glucoside were the major phenolics in V. teucrium. Additionally, the antineurodegenerative activity was tested at concentrations of 25, 50, and 100 µg/ml using acetylcholinesterase (AChE) and tyrosinase (TYR) assays. The inhibition of both enzymes was achieved with the investigated extracts, ranging from 22.78 to 35.40% for AChE and from 9.57 to 16.38% for TYR. There was no statistical difference between the activities of the analyzed extracts. Our data indicate that V. teucrium and V. jacquinii may have beneficial effects against Alzheimer's and Parkinson's disease.

Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3ß inhibitors.

The Monte Carlo method was used for QSAR modeling of maleimide derivatives as glycogen synthase kinase-3ß inhibitors. The first QSAR model was developed for a series of 74 3-anilino-4-arylmaleimide derivatives. The second QSAR model was developed for a series of 177 maleimide derivatives. QSAR models were calculated with the representation of the molecular structure by the simplified molecular input-line entry system. Two splits have been examined: one split into the training and test set for the first QSAR model, and one split into the training, test and validation set for the second. The statistical quality of the developed model is very good. The calculated model for 3-anilino-4-arylmaleimide derivatives had following statistical parameters: r(2)=0.8617 for the training set; r(2)=0.8659, and r(m)(2)=0.7361 for the test set. The calculated model for maleimide derivatives had following statistical parameters: r(2)=0.9435, for the training, r(2)=0.9262 and r(m)(2)=0.8199 for the test and r(2)=0.8418, r(av)(m)(2)=0.7469 and ∆r(m)(2)=0.1476 for the validation set. Structural indicators considered as molecular fragments responsible for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new potential glycogen synthase kinase-3ß inhibitors has been presented by using defined structural alerts.

Application of SMILES Notation Based Optimal Descriptors in Drug Discovery and Design.

SMILES notation based optimal descriptors as a universal tool for the QSAR analysis with further application in drug discovery and design is presented. The basis of this QSAR modeling is Monte Carlo method which has important advantages over other methods, like the possibility of analysis of a QSAR as a random event, is discussed. The advantages of SMILES notation based optimal descriptors in comparison to commonly used descriptors are defined. The published results of QSAR modeling with SMILES notation based optimal descriptors applied for various pharmacologically important endpoints are listed. The presented QSAR modeling approach obeys OECD principles and has mechanistic interpretation with possibility to identify molecular fragments that contribute in positive and negative way to studied biological activity, what is of big importance in computer aided drug design of new compounds with desired activity.